In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 10⁴), retention (≥10⁴ s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co ₄₀ Fe ₄₀ B ₂₀ ) _х (LiNbO _{3− y} ) _{100− х} memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.

The properties of (CoFeB) x(LiNbO y)100– x nanocomposite films with a ferromagnetic alloy content x= 6–48 at % are comprehensively studied. The films are shown to consist of ensembles of CoFe...

We present results of an experimental study of structural, magnetotransport, and magnetic properties of a disordered system which consists of the strained crystalline CoFe nanogranules with the size of 2–5 nm embedded into the B-Al-O oxide matrix with a large number of dispersed Fe or Co atoms. They act in the matrix as magnetic ions and contribute essentially to the magnetization at $T$ ⩽ 25 K. The conductivity of the system follows the $\text{ln}T$ law on the metallic side of the metal-insulator transition in the wide range of metal content variation $x=49-56\mathrm{at}.\%$ that formally corresponds to the conductivity of the array of granules with strong tunnel coupling between them. We found that scaling power laws in the dependence of anomalous Hall effect (AHE) resistivity ${\rho }_{\mathrm{AHE}}$ vs longitudinal resistivity ρ strongly differ if temperature $T$ or metal content $x$ are variable parameters. The obtained results are interpreted in terms of the model of two sources of AHE emf arising from metallic nanogranules and insulating tunneling regions, respectively. We suggest that the tunneling AHE component can be caused by the recently predicted scattering assisted mechanism [A. V. Vedyayev et al., Phys. Rev. Lett. 110, 247204 (2013)] and is strongly shunted due to generation of local circular Hall current.